{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CIFARNet.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"z23zu7AuRTRA","colab_type":"code","colab":{}},"source":["import torch\n","import numpy as np\n","import random\n","\n","random.seed(0)\n","np.random.seed(0)\n","torch.manual_seed(0)\n","torch.cuda.manual_seed(0)\n","torch.backends.cudnn.deterministic = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9E26s0WsRqIg","colab_type":"code","colab":{}},"source":["import torchvision"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"j1F33la4Rva0","colab_type":"code","outputId":"70e5c744-f0a0-4f2a-c8f6-afc30711852b","executionInfo":{"status":"ok","timestamp":1570788578954,"user_tz":-180,"elapsed":13498,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["CIFAR_train = torchvision.datasets.CIFAR10('/', download=True, train=True)\n","CIFAR_test = torchvision.datasets.CIFAR10('/', download=True, train=False)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /cifar-10-python.tar.gz\n"],"name":"stdout"},{"output_type":"stream","text":["170500096it [00:07, 21655169.64it/s]                               \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting /cifar-10-python.tar.gz to /\n","Files already downloaded and verified\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rLK-DDBvSEab","colab_type":"code","colab":{}},"source":["X_train = torch.FloatTensor(CIFAR_train.data)\n","y_train = torch.LongTensor(CIFAR_train.targets)\n","\n","X_test = torch.FloatTensor(CIFAR_test.data)\n","y_test = torch.LongTensor(CIFAR_test.targets)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"751UiptqSijg","colab_type":"code","outputId":"979786a4-0e51-4c18-8a02-fea5712f707c","executionInfo":{"status":"ok","timestamp":1570788580177,"user_tz":-180,"elapsed":14428,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(y_train), len(y_test)"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(50000, 10000)"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"OwDh-q5JSmVv","colab_type":"code","outputId":"6d79530d-9e64-4759-93fd-be0790833ca0","executionInfo":{"status":"ok","timestamp":1570788580557,"user_tz":-180,"elapsed":14610,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.min(), X_train.max()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor(0.), tensor(255.))"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"hQUP8EKIS4Nr","colab_type":"code","colab":{}},"source":["X_train /= 255.\n","X_test /= 255."],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AdexzMpYTEzJ","colab_type":"code","outputId":"149441ba-7270-400a-a7e9-dd2da0a5bde6","executionInfo":{"status":"ok","timestamp":1570788580563,"user_tz":-180,"elapsed":14280,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["CIFAR_train.classes"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['airplane',\n"," 'automobile',\n"," 'bird',\n"," 'cat',\n"," 'deer',\n"," 'dog',\n"," 'frog',\n"," 'horse',\n"," 'ship',\n"," 'truck']"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"36uSl9pXTIoa","colab_type":"code","outputId":"58406740-e86c-49ee-a27b-a1b499251f90","executionInfo":{"status":"ok","timestamp":1570788585467,"user_tz":-180,"elapsed":18996,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import matplotlib.pyplot as plt\n","for i in range(10):\n","  plt.figure(figsize=(22, 20))\n","  plt.subplot(1, 10, i+1)\n","  plt.imshow(X_train[i])\n","  plt.title(CIFAR_train.classes[y_train[i]])\n","  print(CIFAR_train.classes[y_train[i]], end=' ')"],"execution_count":11,"outputs":[{"output_type":"stream","text":["frog truck truck deer automobile automobile bird horse ship cat "],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAI0AAACXCAYAAAAoE9hYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnOZdb37Q9Nt+4M5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Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAI0AAACXCAYAAAAoE9hYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEb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size 1584x1440 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NzCDIb_3Tf4m","colab_type":"code","outputId":"514ea030-91c7-4436-ffa3-ebfe68d611a4","executionInfo":{"status":"ok","timestamp":1570788585476,"user_tz":-180,"elapsed":18901,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, y_train.shape"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([50000, 32, 32, 3]), torch.Size([50000]))"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"pIAFzEIzVGc2","colab_type":"code","colab":{}},"source":["X_train = X_train.permute(0, 3, 1, 2)\n","X_test = X_test.permute(0, 3, 1, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"j39SSvC2VS-L","colab_type":"code","outputId":"4847109b-1221-4252-82c2-87c638c299ae","executionInfo":{"status":"ok","timestamp":1570788585484,"user_tz":-180,"elapsed":18537,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, y_train.shape"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([50000, 3, 32, 32]), torch.Size([50000]))"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"0Y6cktgFVZOb","colab_type":"code","colab":{}},"source":["class LeNet5(torch.nn.Module):\n","  def __init__(self,\n","               activation='tanh',\n","               pooling='avg',\n","               conv_size=5,\n","               use_batch_norm=False):\n","    super().__init__()\n","    \n","    self.conv_size = conv_size\n","    self.use_batch_norm = use_batch_norm\n","    \n","    if activation == 'tanh':\n","      activ_func = torch.nn.Tanh()\n","    elif activation == 'relu':\n","      activ_func = torch.nn.ReLU()\n","    else:\n","      raise 'Take a dick'\n","      \n","    if pooling == 'avg':\n","      pool_layer = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n","    elif pooling == 'max':\n","      pool_layer = torch.nn.MaxPool2d(kernel_size=2, stride=2)\n","    else:\n","      raise 'Take a new dick'\n","      \n","    if conv_size == 5:\n","      self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=6,\n","                                   kernel_size=5, padding=0)\n","    elif conv_size == 3:\n","      self.conv1_1 = torch.nn.Conv2d(in_channels=3, out_channels=6,\n","                                     kernel_size=3, padding=0)\n","      self.conv1_2 = torch.nn.Conv2d(in_channels=6, out_channels=6,\n","                                     kernel_size=3, padding=0)\n","    else:\n","      raise \"May be u wana tis? (it's dick)\"\n","      \n","    self.act1 = activ_func\n","    self.bn1 = torch.nn.BatchNorm2d(num_features=6)\n","    self.pool1 = pool_layer\n","    \n","    if conv_size == 5:\n","      self.conv2 = torch.nn.Conv2d(in_channels=6, out_channels=16,\n","                                  kernel_size=5, padding=0)\n","    elif conv_size == 3:\n","      self.conv2_1 = torch.nn.Conv2d(in_channels=6, out_channels=16,\n","                                     kernel_size=3, padding=0)\n","      self.conv2_2 = torch.nn.Conv2d(in_channels=16, out_channels=16,\n","                                     kernel_size=3, padding=0)\n","    else:\n","      raise 'Soooo ... it is for you! (it is dick)'\n","      \n","    self.act2 = activ_func\n","    self.bn2 = torch.nn.BatchNorm2d(num_features=16)\n","    self.pool2 = pool_layer\n","    \n","    if conv_size == 5:\n","      self.conv3 = torch.nn.Conv2d(in_channels=16, out_channels=120,\n","                                   kernel_size=5, padding=0)\n","    elif conv_size == 3:\n","      self.conv3_1 = torch.nn.Conv2d(in_channels=16, out_channels=120,\n","                                     kernel_size=3, padding=0)\n","      self.conv3_2 = torch.nn.Conv2d(in_channels=120, out_channels=120,\n","                                     kernel_size=3, padding=0)\n","    else:\n","      raise 'Somebody says, that it is your'\n","      \n","    self.act3 = activ_func\n","    self.bn3 = torch.nn.BatchNorm2d(num_features=120)\n","    \n","    self.fc1 = torch.nn.Linear(120, 84)\n","    self.act4 = activ_func\n","    \n","    self.fc2 = torch.nn.Linear(84, 10)\n","    \n","    self.sm = torch.nn.Softmax()\n","    \n","  def forward(self, x):\n","    if self.conv_size == 5:\n","      x = self.conv1(x)\n","    elif self.conv_size == 3:\n","      x = self.conv1_2(self.conv1_1(x))\n","    \n","    x = self.act1(x)\n","    \n","    if self.use_batch_norm:\n","      x = self.bn1(x)\n","    \n","    x = self.pool1(x)\n","    \n","    if self.conv_size == 5:\n","      x = self.conv2(x)\n","    elif self.conv_size == 3:\n","      x = self.conv2_2(self.conv2_1(x))\n","    \n","    x = self.act2(x)\n","    \n","    if self.use_batch_norm:\n","      x = self.bn2(x)\n","    \n","    x = self.pool2(x)\n","    \n","    if self.conv_size == 5:\n","      x = self.conv3(x)\n","    elif self.conv_size == 3:\n","      x = self.conv3_2(self.conv3_1(x))\n","      \n","    x = self.act3(x)\n","    \n","    if self.use_batch_norm:\n","      x = self.bn3(x)\n","          \n","    x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))\n","    \n","    x = self.fc1(x)\n","    x = self.act4(x)\n","    x = self.fc2(x)\n","    \n","    return x\n","  \n","  def prediction(self, x):\n","    x = self.forward(x)\n","    x = self.sm(x)\n","    \n","    return x\n","    \n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9_rzy9l161Is","colab_type":"code","colab":{}},"source":["def train(net, X_train, y_trian, X_test, y_test):\n","  device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","  net = net.to(device)\n","  loss = torch.nn.CrossEntropyLoss()\n","  optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n","  \n","  batch_size = 100\n","  \n","  test_accuracy_history = []\n","  test_loss_history = []\n","  \n","  X_test = X_test.to(device)\n","  y_test = y_test.to(device)\n","  \n","  for epoch in range(30):\n","    order = np.random.permutation(len(X_train))\n","    \n","    for start_index in range(0, len(X_train), batch_size):\n","      optimizer.zero_grad()\n","      net.train()\n","      \n","      batch_indices = order[start_index: start_index+batch_size]\n","      \n","      X_batch = X_train[batch_indices].to(device)\n","      y_batch = y_trian[batch_indices].to(device)\n","      \n","      y_pred = net.forward(X_batch)\n","      \n","      loss_value = loss(y_pred, y_batch)\n","      loss_value.backward()\n","      \n","      optimizer.step()\n","      \n","    net.eval()\n","    \n","    test_preds = net.forward(X_test)\n","    test_loss_history.append(loss(test_preds, y_test).data.cpu())\n","    \n","    accuracy = (test_preds.argmax(dim=1) == y_test).float().mean().data.cpu()\n","    test_accuracy_history.append(accuracy)\n","    \n","    print(accuracy)\n","  \n","  print('-----so... training is over----')\n","  del net\n","  return test_accuracy_history, test_loss_history"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7U9cwFq2S6Ft","colab_type":"code","colab":{}},"source":["accuracies = {}\n","losses = {}\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tQcIobPr-QYH","colab_type":"code","outputId":"34ae6cb1-fa59-4631-9090-786c950fb415","executionInfo":{"status":"ok","timestamp":1570788901892,"user_tz":-180,"elapsed":294230,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","accuracies['tanh'], losses['tanh'] = train(LeNet5(activation='tanh', \n","                                                  conv_size=5),\n","                                           X_train, y_train, \n","                                           X_test, y_test)\n","\n","# accuracies['relu'], losses['relu'] = train(LeNet5(activation='relu',\n","#                                                   conv_size=5),\n","#                                            X_train, y_train,\n","#                                            X_test, y_test)\n","\n","# accuracies['relu_3'], losses['relu_3'] = train(LeNet5(activation='relu',\n","#                                                      conv_size=3),\n","#                                               X_train, y_train,\n","#                                               X_test, y_test)\n","\n","# accuracies['relu_3_max'], losses['relu_3_max'] = train(LeNet5(activation='relu',\n","#                                                               conv_size=3,\n","#                                                               pooling='max'),\n","#                                                        X_train, y_train,\n","#                                                        X_test, y_test)\n","\n","accuracies['relu_3_max_bn'], losses['relu_3_max_bn'] = train(LeNet5(activation='relu',\n","                                                                    conv_size=3,\n","                                                                    pooling='max',\n","                                                                    use_batch_norm=True),\n","                                                             X_train, y_train,\n","                                                             X_test, y_test)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["tensor(0.3876)\n","tensor(0.4333)\n","tensor(0.4709)\n","tensor(0.4961)\n","tensor(0.4967)\n","tensor(0.5179)\n","tensor(0.5272)\n","tensor(0.5360)\n","tensor(0.5430)\n","tensor(0.5406)\n","tensor(0.5456)\n","tensor(0.5456)\n","tensor(0.5425)\n","tensor(0.5529)\n","tensor(0.5570)\n","tensor(0.5595)\n","tensor(0.5546)\n","tensor(0.5528)\n","tensor(0.5551)\n","tensor(0.5524)\n","tensor(0.5539)\n","tensor(0.5515)\n","tensor(0.5441)\n","tensor(0.5498)\n","tensor(0.5465)\n","tensor(0.5474)\n","tensor(0.5432)\n","tensor(0.5438)\n","tensor(0.5412)\n","tensor(0.5396)\n","-----so... training is over----\n","tensor(0.5591)\n","tensor(0.6031)\n","tensor(0.6389)\n","tensor(0.6396)\n","tensor(0.6545)\n","tensor(0.6599)\n","tensor(0.6581)\n","tensor(0.6722)\n","tensor(0.6840)\n","tensor(0.6688)\n","tensor(0.6729)\n","tensor(0.6824)\n","tensor(0.6865)\n","tensor(0.6751)\n","tensor(0.6830)\n","tensor(0.6782)\n","tensor(0.6864)\n","tensor(0.6794)\n","tensor(0.6767)\n","tensor(0.6819)\n","tensor(0.6740)\n","tensor(0.6619)\n","tensor(0.6709)\n","tensor(0.6742)\n","tensor(0.6735)\n","tensor(0.6771)\n","tensor(0.6694)\n","tensor(0.6659)\n","tensor(0.6712)\n","tensor(0.6742)\n","-----so... training is over----\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dbAsIEx6AL7L","colab_type":"code","outputId":"6316bf0d-c278-47ee-e3b1-69027404a34a","executionInfo":{"status":"ok","timestamp":1570788902441,"user_tz":-180,"elapsed":293601,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["for experiment_id in accuracies.keys():\n","  plt.plot(accuracies[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('Val Accuracy')"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Val Accuracy')"]},"metadata":{"tags":[]},"execution_count":19},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VPX1+P/XyUISsgHZgLAk7KuA\nRBZFRCuKK7YuVWtFbbVu35/9dHXpp3Vra1u1th+t1h2tilIVse5WUVkUgrIGhAABEpZsZN8z5/fH\nnUAIWSbJkElmzvPxmEfm7udmknPvnPt+3yuqijHGmMAQ5OsAjDHGdB1L+sYYE0As6RtjTACxpG+M\nMQHEkr4xxgQQS/rGGBNALOkbvyMiKSKiIhLi61iM6W4s6ZtuR0TeF5F7mxk/X0QOeCuZi8gyETkk\nImHeWJ8xPYElfdMdLQSuEhFpMv6HwEuqWtfZDYhICnAqoMCFnV1fO7dt30CMz1jSN93REiAOJykD\nICJ9gfOBF9zD54nINyJSIiJ7ReTudm7jauBL4HlgQeMJIhIhIg+JyG4RKRaR5SIS4Z42S0RWikiR\ne7vXuMcvE5EfN1rHNSKyvNGwisgtIrId2O4e9zf3OkpEZK2INN7fYBG5U0R2iEipe/pgEXlMRB5q\nEu9SEfmfdu6/CVCW9E23o6qVwGs4ibnBZcBWVV3vHi53T+8DnAfcJCIXtWMzVwMvuV9ni0hSo2kP\nAlOBk4F+wK8Al4gMBd4D/g9IACYD69qxzYuA6cA49/Aa9zr6AS8Di0Uk3D3tZ8AVwLlADHAdUIHz\nLegKEQkCEJF44Ez38sa0yZK+6a4WApc0SoJXu8cBoKrLVHWjqrpUdQPwCnCaJysWkVnAUOA1VV0L\n7ACudE8Lwkmwt6lqjqrWq+pKVa12z/Oxqr6iqrWqWqCq7Un6f1TVQvdBDVX9l3sddar6EBAGjHbP\n+2PgN6r6rTrWu+ddDRQD33HPdzmwTFUPtiMOE8As6ZtuSVWXA/nARSIyHJhGo7NZEZkuIp+KSJ6I\nFAM3AvEern4B8KGq5ruHX+ZIiSceCMc5EDQ1uIXxntrbeEBEfiEiW9wlpCIgliP70Nq2FgJXud9f\nBbzYiZhMgLELSqY7ewHnDH808EGTs9mXgUeBc1S1SkQewYOk767NXwYEi8gB9+gwoI+ITAI2AlXA\ncGB9k8X34hx8mlMO9G403L+ZeQ7f0tZdv/8Vzhn7ZlV1icghoOHi9V53DJuaWc+/gE3ueMfiXAMx\nxiN2pm+6sxdw6tXX06i04xYNFLoT/jTc5RkPXATU49TVJ7tfY4EvgKtV1QU8CzwsIgPdF1Rnupt1\nvgScKSKXiUiIiMSJyGT3etcB3xOR3iIyAvhRG3FEA3VAHhAiIr/Fqd03eBq4T0RGiuMEEYkDUNVs\nnOsBLwKvN5SLjPGEJX3TbalqFrASiASWNpl8M3CviJQCv8W58OuJBcBzqrpHVQ80vHC+NfzA3Zzy\nFzhn/GuAQuBPQJCq7sG5sPpz9/h1wCT3ev8K1AAHcQ5QL7URxwfA+8A2YDfOt4vG5Z+H3fv0IVAC\nPANENJq+EJiIlXZMO4k9RMWYnkdEZuOUeYaq/RObdrAzfWN6GBEJBW4DnraEb9rLkr4xPYiIjAWK\ngAHAIz4Ox/RAHiV9EZknIt+KSKaI3N7M9L+KyDr3a5u7+VnDtAUist39WtB0WWOM51R1i6pGqurJ\nqlri63hMz9NmTV9EgnEuNs0FGloNXKGqGS3M//+AKap6nYj0A9KBNJzmamuBqap6yHu7YIwxxlOe\ntNOfBmSq6k4AEVkEzAeaTfo4Xcd/535/NvCRqha6l/0ImIfTe7JZ8fHxmpKS4lHwxhhjHGvXrs1X\n1YS25vMk6SdzdFOybJz7hxzDfW+SVOCTVpZNbma5G4AbAIYMGUJ6eroHYRljjGkgIrs9mc/bF3Iv\nB/6tqvXtWUhVn1TVNFVNS0ho80BljDGmgzxJ+jk49wFpMMg9rjmXc3Tppj3LGmOMOc48SfprgJEi\nkioivXASe9PekYjIGKAvsKrR6A+As0Skr/t+6Ge5xxljjPGBNmv6qlonIrfiJOtg4FlV3ex+nF26\nqjYcAC4HFjXuLKKqhSJyH86BA+Dehou6xhhjul63uw1DWlqa2oVcY4xpHxFZq6ppbc1nPXKNMSaA\nWNI3xpgAYknf9Fx11ZC1HL58HIqzfR1N66pKYOO/4ZBHTamNOW7syVmm56ivg/3rYNdnsOtz2PMl\n1FU505b9ES74O4xvz7PRu0BNBax5CpY/ApWFIEEw9gKYcTMMng4iba/DGC+ypG+at3cNfPw7mH4j\njLvQNzG4XJCb4ST4XZ/D7hVQ7b7HWOJ4mHotpM6G2GR4+6eweAFk/hDO+RP0iuzctouzISoJgkM7\ntnxdNaxdCF88CGUHYcSZTqLf9TmsfR4y3oKBJ8LMW2Dc/I5vx5h2stY75mguF6z6P/jvvaAKKFz0\nBEz6ftfFUFUCn/0J1i+CCvezy/sNdxJ86mxIORWimvTcrq+FT/8Ay/8KccPh4mdg4ORj192W3Sth\n2QPOt4mwWBh5Jow6x/kZ0bft5etrYd3L8NmfoSQbhs6CM34DQ2cemaem3JnnqyegIBNikmHa9TD1\nGs+20V61lZD5X+dbxah5EBTs/W0Yn/O09Y4lfXNEeQEsuRG2fwhjL3TOmN/8Cez6As7/K6Rde3y3\nrwobF8OHv4GyXKdUM/Is99n8IM/WsetzeOMnUJ4H3/ktzLwVgjy4dJW1Aj57wFk+MgFOuh6K98C2\nD5x1STAMPdlJmqPPcQ4sjbnqYdPrTpmpcCckT4Uz/heGzWm5hONyQeZHsOox5yAT2hsmXwnTb4L4\nEZ7tb0uqy5x1Z7wF2z6E2nJnfPwoOO3XMP67lvz9jCV90z5ZK+D1H0FFAZz9Bzjpx06yqq2E1652\nDgRn/xFm3nx8tn9wM7z7S6eEM/BEOO9BJ3F2REUhLP1/sPU/MOx0+O4TEN2/+Xmzljtn9llfQGQi\nzPqpUzbq1duZ7nJBzlrY9h58+55TbgIneY6aB6PPhfJc51tG3lZImuic2Y86u331+gObnAvSG1+D\n+hoYPAOSxkPiWPdrHPTu1/o6qkqcg1TGEsj82LneEZngXEMYNx8qDznfQHIznPhn/xImXGzJ309Y\n0jeecdXDFw/Dsj9A31S49DkYMOnoeepqnAPClqVOQpv9S+9tv6oYPv0jrH4SwmPhzLthyg89Oztv\njapTO3//DieBz/8HjJ53ZPquL5xkv3u5U7s/5adOeaUh2bfkUBZ8+75zEMhaAa5aZ3z8KDj9Thg7\nv3Oxl+VC+rOw4xPI3XLkGgY4B6XDBwH3gSAm2fl2kvEW7Pivc8CIHnAk0Q+ZeXRSd7mcz/GzP0Pu\nZogbeST5B/vhJT5XvXOQC+197LczP2NJ37St9CC8cb1TWph4qVPCCYtuft76OnjrZtjwKsz6mVM6\n6UzLE1VnXR/+r1M+SbvWKYe0dTbbXnnb4PXr4MBGp2Qz+hz44iHnG0VUf/eZ/TUQGtH+dVcVO8kZ\ncZKst8+YVaFkn5P887Y4P3O3ON8oaiuOnjdmkJPkx82HQSe1feBxuWDr207yP7gJ4ka4k/8lPTv5\nVxZBTjrsXQ17v4LsdKgpc6aNvRBm/+LYkxo/YUnftG7Hp/DGDVBdCuf+2Tm7biuJu1zwn5/C1wud\nVj3zHuhY4j+w0Snl7FnllHDOfRCST+zYfniirtq5ML3qUWc4qj+c+jM48eqOJXtfc7mgaLeT/A9l\nwaBpzu+vI5+Fy+WUwT77Mxzc6Fwwn/1LOOH7nf+21V4VhbDvG6fEGNrb+dYVGum0xGr8PrS3E5uq\nc/1k71fu12rnoIg6TWOTxjtlssHTIH8bfPVP55vTqHlw6i9g8Eldu38tUYXSA87/hasOxpzbodVY\n0jfNq69zLlh+/qBTkrj0eUga5/nyqk7J5KvH4cQFzrcDT85wa6sgew1sfhPWPgfhfWDuPTD5qq5L\nLjs/g0O74ITLITS8a7bZU7hc8O27zt/GgY0w/DvwvacgMu74bK+mHPavh5yvYd/Xzs9DuzxfPiTC\n+btrOIsPi3WS+ODpTpJPnnrst9bKIqfPxKp/OH0mhs1xDnAps7y1V21z1UPBDjiwwfk9N/wsz3Om\nJ02Am1Z0aNWW9M2xqkudi7I7PnGS7bl/7lh7dlX45D6nTDLxMrjo8WNLAg0dqXYuc2rOe79yLixK\nMExdcHxKOabzVJ2D8nu3Q2S8c1IweFrn13tgk/M3sO9ryPnGKVepy5kWMwiSpzgX8JNPdK5T1FY4\nB4aaCqflUU2Fk+BrK46Mq6txTlgGT4f40Z6fPFSXOfu44u/ORfghJztln+FneL+zXOlB2P4B7Fvn\nJPiDm4+U5oJCIXEM9J8E/Se6XxOca1sdYEnfHK30ALx0qfNHd/5fncTbWZ8/6CT/sRfA956Ggu1H\nOlJlrYCaUme+pAlH2tgPPbnDf9SmC+1b53R2K86GuffBjJs6lhDztjlNcLe7H6MR0c9J7A0JfuCJ\nEJ3k3dg9VVsJX78IKx6BkhwnllN/7nSk68w3wdKDzsXyzUuca0cohMW4k/oJRxJ8whgI6eW13bGk\nb47I2wb/utiplV62EEbO9d66V/0DPrgDgsOgvtoZ1284DDvtSEeqyHjvbc90ncoieOsWp+Y/9kKY\n/6jnB+yKQqd1VPozTg3+1J85fQP6DO1+t56oq4H1r8Dyh51rJEGhMO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hew5QhfbjptOGc\nOTap1YvKmbllLPkmhyXrcsg+VEl4aBBnj+/PkH692ZBdzIbsIg5V1ALOQXHcgBgmNRwIBscyLD4K\nEaiuc1FaVUdpVS1l1XXu90eGy6rqiO0dysnD4xmeEOm31yW8WdOfCdytqme7h+8AUNU/tjB/MFCo\nqsd8jxWR54H/qOq/W9qe1fT9V3l1HauzCqmqadrGvXkxEaGMGxBD38jWyxuNvb/pAL9Zsomiihpu\nmjOcW88YQVhI9zy79zeVNfUsXruXJz/fSfahSkYkRvGT2cOYPzn58Fl6bmkVb6/fz5JvctiYU0yQ\nwCkj4vnulGTOGt+fqLAjxQdVJftQJev2FrEhu4j12cVsyimmwv33ExYShEv1qOsObUmKCeOUEfHM\nGhHPKSPiSYoJ9+4vwYe8mfRDcEo238FpnbMGuFJVNzeaZ4Cq7ne//y7wa1WdISJ9gQr3N4B4YBUw\nv/FF4KYs6fuXwvIaPt5ykA83H+CL7flU1x17EbEtyX0imJAcw4SBsUxIjmV8cgyJ0Uf/s+aXVfO7\ntzbzzsb9jB8Yw58vOYHxA5uvn5vjq67exTsb9/P4sh1sPVDKgNhwLpk6iPXZxSzfnodLYUJyDBdN\nTubCSQNJbEfirXcpmbllrM8uYtuBUkJDgogODyE6LITo8FCiwkKIDg8hKjyEGPdwVHgI+4uqWLEj\nn+WZ+azMzD/8DWJEYhSzRsRz8vA4ZgyPIyY8tMVtu1xKVV09VbUuKmvriewV3Ob1lq7k7Sab5wKP\n4DTZfFZVfy8i9wLpqrpURP4IXIhTty8EblLVrSJyMvBPwIVzn59HVPWZ1rZlSb97yNhXwpJ1OUSF\nhRxusZEaH+lRTXxvYQUfZjiJfk2W01kpuU8Ec92dleKiPPtHyS+tYfO+YjbtK2FzTjE788sPT0uM\nDmNCciwTBsYQExHKY59mUl5dz21njuSG2cMIDbb6r6+pKsu25fHEsh18tauQ5D4RXDRlIBdNTmZk\nUrTP4nK5lC0HSliRmc/yzAJW7yqgqtZFkMC4gTGEBgdRWVNPdZ2Lypp6Kmvrqaqtb/aEJT4qjBGJ\nkYxMjGZkktNpb2RiNPFRXd+81TpnmXZzuZRPv83lmeW7WLmjgNBgOearc3KfCFLjIw832UtNiGJY\nfCTlNXV8sOkgH2YcYPO+EgBGJ0Vz1vgkzh7f3yu3JSitqmXL/lI25RSzaV8xm3NK2J5bikthypA+\n/PniE3yaTEzLckuriI8M65Ydx6rr6vlmTxErMvP5Zk8RIhAeGkxEaDDhoUHOz17BhIcEE9HryPiS\nyjq255ayPbeMzINlR/WX6NM7lJGJUYxIjGZEYhQDY8NJjAknKcZpMXU8So6W9I3HKmrqeH1tNs+t\nyGJnfjkDYsNZcHIKV5w0hNAQISu/gp35zj1nnGZ7zvvGf+Tg3PLoxCF9OWtcEmeN709qfORxj72y\npp7sQxUMS4jySocqYzpCVTmN8NTMAAAXvUlEQVRYUs323FIyc8sOHwi25ZZS5C4lNda3dyhJMe4D\nQXSY+30YKXGRzB6V0KEYLOmbNu0vrmThyt28snoPxZW1TBrchx/NSuWcCf3bLI+oKnll1ezKK2dn\nfjlBAqePSTym1m5MIFNVDlXUcqC4ioOlVeSWVHGwpJrcUvdP93BeWTX1LuXEIX144+ZTOrQtb3bO\nMn5m3d4inlm+i3c37kdVOWfCAK6blcKJQ/p6XIIRERKjw0mMDmf6sI61AzfG34kI/SJ70S+yF+No\nufNavUspKK+mupne0t5mST+AbN5XzB/f3cryzHyiw0K47pQUFpycwqC+dh94Y3wpOEi67FuyJf0A\nsL+4kgc/2MYb32TTJyKU35w3lsunDTmqTbQxJjDYf70fK6uu44llO3jqi50ocMPsYdw8ZwSxES23\nRTbG+DdL+n6ort7FojV7eeTjbeSX1TB/8kB+cdZoe5yfMcaSvj9RVT7Zmssf3t3CjrxypqX245kF\nY5k0uI+vQzPGdBOW9P3Eppxifv/OFlbtLGBYfCRP/nAqc8cl+e3NpYwxHWNJv4fLL6vmL+9/y2tr\n99K3dy/unT+eK6YNsdsQGGOaZUm/h6qtd/HCqt088tE2Kmvruf7UYdx6xohWbxhljDGW9Hug5dvz\nufvtzWTmljF7VAK/PX8cIxI9fiKlMSaAWdLvQfYWVnD/Oxl8sPkgQ/r15umr0/jO2ESr2xtjPGZJ\nvweorKnn8WWZPPH5ToJF+OXZo/nRrFR7jqgxpt0s6Xdjqso7G/fzh3e2sK+4ivmTB3L7OWMYEBvh\n69CMMT2UJf1u7K4lm3j5qz2MHRDDI5dPYVpqP1+HZIzp4Szpd1NvfJ3Ny1/t4UezUrnz3LF2r3hj\njFdYY+5uKDO3lLve3MS0lH7ccc4YS/jGGK+xpN/NVNbUc8tL3xDRK5i/XzGFEOtkZYzxIivvdDP3\nvL2Zbw+WsvC6afSPtadQGWO8y04ju5E3v8lm0Zq93DxnOKd18DmZxhjTGkv63URmbhl3vbmJk1L6\n8rO5o3wdjjHGT1nS7waqauu59eWvCQ+1Or4x5viymn43cM/bm9l6oJTnrz3JOl4ZY44rj04pRWSe\niHwrIpkicnsz068RkTwRWed+/bjRtAUist39WuDN4P3BW+tyeGX1Xm6aM5w5oxN9HY4xxs+1eaYv\nIsHAY8BcIBtYIyJLVTWjyayvquqtTZbtB/wOSAMUWOte9pBXou/hduSVcecbG0kb2pefWx3fGNMF\nPDnTnwZkqupOVa0BFgHzPVz/2cBHqlroTvQfAfM6Fqp/qaqt55aXvqZXSBD/d6XV8Y0xXcOTTJMM\n7G00nO0e19TFIrJBRP4tIoPbuWzAueftDLYeKOXhyyZbHd8Y02W8dXr5NpCiqifgnM0vbM/CInKD\niKSLSHpeXp6XQuq+nDr+Hm48bTinj7E6vjGm63jSeicHGNxoeJB73GGqWtBo8Gngz42WndNk2WVN\nN6CqTwJPAqSlpakHMXU7G7KLeGX1Xipr6qisraey1kVVbT1VtfVU1tRTWVtPlXtceU0dU4f25edn\nWR3fGNO1PEn6a4CRIpKKk8QvB65sPIOIDFDV/e7BC4Et7vcfAH8Qkb7u4bOAOzoddTdTUlXL9S+k\nU1ZVR3x0GBGhwYSFBhMRGkRcZC/C+wQ3GhdMTEQIP5g+1B5ebozpcm0mfVWtE5FbcRJ4MPCsqm4W\nkXuBdFVdCvx/InIhUAcUAte4ly0UkftwDhwA96pq4XHYD5968INvySut5s2bT2HS4D6+DscYY1ok\nqt2rmpKWlqbp6em+DsNjX+85xMWPr2TBzBTuvnC8r8MxxgQoEVmrqmltzWf1hU6orXdxx+sb6R8T\nzi/OHu3rcIwxpk12G4ZOeOqLnXx7sJQnfziVqDD7VRpjuj870++g3QXl/O3j7cwb35+zxvf3dTjG\nGOMRS/odoKr8ZskmQoODrI5vjOlRLOl3wJJ1OXyxPZ9fzRttT7cyxvQolvTb6VB5Dff9ZwuTB/fh\nB9OH+jocY4xpF0v67fSHd7dQUlnLH783keAg8XU4xhjTLpb022HljnwWr83m+tnDGDsgxtfhGGNM\nu1nS91BVbT13vbmJIf16c9t3Rvo6HGOM6RBrXO6hf3yaya78cl780TTCQ4N9HY4xxnSInel7YPvB\nUh7/bAcXTR7IqSMTfB2OMcZ0mCX9Nrhcyp1vbiQyLITfnD/O1+EYY0ynWNJvw6I1e1mTdYg7zx1L\nfFSYr8MxxphOsaTfiuLKWh54bwszhvXj0qmDfB2OMcZ0miX9VixO30tJVR2/OW8cItYm3xjT81nS\nb4HLpbz45W5OSunLhORYX4djjDFeYUm/BZ9tz2N3QQVXz0zxdSjGGOM1lvRb8MLKLBKjwzjbbpts\njPEjlvSbkZVfzrJteVw5fQi9QuxXZIzxH5bRmvGvL3cTLMKV04b4OhRjjPEqS/pNVNTU8Vr6Xs6Z\nOIDEGLtXvjHGv1jSb+KtdfsoqapjwUy7V74xxv9Y0m9EVVm4MotxA2KYOrSvr8Mxxhivs6TfyJqs\nQ2w9UMqCk4daZyxjjF/yKOmLyDwR+VZEMkXk9lbmu1hEVETS3MMpIlIpIuvcrye8FfjxsHBVFrER\noVw4KdnXoRhjzHHR5v30RSQYeAyYC2QDa0RkqapmNJkvGrgN+KrJKnao6mQvxXvcHCiu4oNNB7hu\nVioRvex++cYY/+TJmf40IFNVd6pqDbAImN/MfPcBfwKqvBhfl3l59R7qVbnKHnZujPFjniT9ZGBv\no+Fs97jDROREYLCqvtPM8qki8o2IfCYip3Y81OOnps7Fy1/t4YzRiQyJ6+3rcIwx5rjp9OMSRSQI\neBi4ppnJ+4EhqlogIlOBJSIyXlVLmqzjBuAGgCFDur5D1Hub9pNfVs3VJ6d0+baNMaYreXKmnwMM\nbjQ8yD2uQTQwAVgmIlnADGCpiKSparWqFgCo6lpgBzCq6QZU9UlVTVPVtISErn8c4QurdpMaH8mp\nI+K7fNvGGNOVPEn6a4CRIpIqIr2Ay4GlDRNVtVhV41U1RVVTgC+BC1U1XUQS3BeCEZFhwEhgp9f3\nohM25RSzdvchfjhjKEFB1kzTGOPf2izvqGqdiNwKfAAEA8+q6mYRuRdIV9WlrSw+G7hXRGoBF3Cj\nqhZ6I3BveWFVFr17BXOxPRnLGBMAPKrpq+q7wLtNxv22hXnnNHr/OvB6J+I7rg6V1/DWun1cMnUQ\nsRGhvg7HGGOOu4Dukfta+l6q61z2oBRjTMAI2KRf734c4oxh/RjdP9rX4RhjTJcI2KT/6dZcsg9V\nco010zTGBJCATfoLV2UxIDacM8cm+ToUY4zpMgGZ9HfklfHF9nyumjGUkOCA/BUYYwJUQGa8l77c\nQ6/gIL5/0uC2ZzbGGD8ScEm/3qW8vWEfZ4xJJD4qzNfhGGNMl+r0vXd6mq92FpBXWs2Fkwf6OhRj\nfKq2tpbs7GyqqnrkjXEDVnh4OIMGDSI0tGN9iwIu6S9dv4+osBDOGJPo61CM8ans7Gyio6NJSUmx\nJ8X1EKpKQUEB2dnZpKamdmgdAVXeqalz8d6mA5w1LonwUHtQiglsVVVVxMXFWcLvQUSEuLi4Tn07\nC6ik//m2PIora7nASjvGAFjC74E6+5kFVNJfun4ffXuHMstuoWyMCVABk/Qraur4KOMg50wcQKi1\nzTfG54qKivjHP/7R4eXnzJlDenq6FyMKDAGT/T7ekktlbT0XTrLSjjHdQWeTvumYgGm9s3TdPvrH\nhDMtpZ+vQzGm27nn7c1k7Ctpe8Z2GDcwht9dML7F6bfffjs7duxg8uTJnH766WzYsIFDhw5RW1vL\n/fffz/z588nKyuKcc85h1qxZrFy5kuTkZN566y0iIiIAWLx4MTfffDNFRUU888wznHpqt3wMd7cS\nEGf6xRW1fLYtl/NPGGBPxzKmm3jggQcYPnw469at4y9/+QtvvvkmX3/9NZ9++ik///nPUVUAtm/f\nzi233MLmzZvp06cPr79+5BEddXV1rF69mkceeYR77rnHV7vSowTEmf77m/dTW6/WIcuYFrR2Rt4V\nVJU777yTzz//nKCgIHJycjh48CAAqampTJ48GYCpU6eSlZV1eLnvfe97zY43LQuIpP/2+v2kxPVm\nYnKsr0MxxjTjpZdeIi8vj7Vr1xIaGkpKSsrhtuhhYUdulxIcHExlZeXh4YZpwcHB1NXVdW3QPZTf\nl3dyS6tYuSOfCycNtDbJxnQj0dHRlJaWAlBcXExiYiKhoaF8+umn7N6928fR+S+/P9N/d8N+XIqV\ndozpZuLi4jjllFOYMGECJ510Elu3bmXixImkpaUxZswYX4fnt/w+6S9dv4+xA2IYkWiPRDSmu3n5\n5ZfbnGfTpk2H3//iF784/H7ZsmWH38fHx1tN30N+Xd7ZW1jB13uKrG2+Mca4+XXSf3vDPgDOP2GA\njyMxxpjuwa+T/tJ1+zhxSB8G9+vt61CMMaZb8Nukv/1gKVsPlFppxxhjGvEo6YvIPBH5VkQyReT2\nVua7WERURNIajbvDvdy3InK2N4L2xNL1+wgSOO8ES/rGGNOgzdY7IhIMPAbMBbKBNSKyVFUzmswX\nDdwGfNVo3DjgcmA8MBD4WERGqWq993bhWKrK0vX7OHl4PAnR9hxcY4xp4MmZ/jQgU1V3qmoNsAiY\n38x89wF/Aho/0mU+sEhVq1V1F5DpXt9xtSG7mN0FFVbaMcaYJjxJ+snA3kbD2e5xh4nIicBgVX2n\nvcu6l79BRNJFJD0vL8+jwFvz9vp99AoO4uwJ/Tu9LmOM73X03vlPPPEEEydOZPLkycyaNYuMjIy2\nF+oCvnwWQKc7Z4lIEPAwcE1H16GqTwJPAqSlpWln4nG5lP9s2M9poxOIjejY0+KNCTjv3Q4HNnp3\nnf0nwjkPeDy7qqKqBAV5r33JlVdeyY033gjA0qVL+dnPfsb777/vtfX3RJ78dnOAwY2GB7nHNYgG\nJgDLRCQLmAEsdV/MbWtZr1udVciBkior7RjTA2RlZTF69GiuvvpqJkyYwIsvvsjMmTM58cQTufTS\nSykrKztmmaioqMPv//3vf3PNNde0uP6YmJjD78vLy1u9/9bzzz/PRRddxNy5c0lJSeHRRx/l4Ycf\nZsqUKcyYMYPCwkIAnnrqKU466SQmTZrExRdfTEVFBQDz58/nhRdeAOCf//wnP/jBD1rd9xdffJHJ\nkyczYcIEVq9eDcDdd9/Nddddx5w5cxg2bBh///vfW11HhzQcXVt64Xwb2AmkAr2A9cD4VuZfBqS5\n3493zx/mXn4nENza9qZOnaqdcccbG3Ts/76n5dW1nVqPMf4uIyPD1yHorl27VER01apVmpeXp6ee\neqqWlZWpquoDDzyg99xzj6qqnnbaabpmzRpVVY2MjDy8/OLFi3XBggWtbuPRRx/VYcOG6aBBg3Tb\ntm0tzvfcc8/p8OHDtaSkRHNzczUmJkYff/xxVVX96U9/qn/9619VVTU/P//wMnfddZf+/e9/V1XV\nAwcO6PDhw/Xzzz/XkSNHakFBQYvbOu200/THP/6xqqp+9tlnOn78eFVV/d3vfqczZ87UqqoqzcvL\n0379+mlNTc0xyzf32QHp2kY+V9W2z/RVtQ64FfgA2AK8pqqbReReEbmwjWU3A68BGcD7wC16HFvu\n1Na7eG/jfs4cm0TvXn5/WyFj/MLQoUOZMWMGX375JRkZGZxyyilMnjyZhQsXeuVum7fccgs7duzg\nT3/6E/fff3+r855++ulER0eTkJBAbGwsF1xwAQATJ048fG+fTZs2ceqppzJx4kReeuklNm/eDEBS\nUhL33nsvp59+Og899BD9+rX+lL4rrrgCgNmzZ1NSUkJRUREA5513HmFhYcTHx5OYmHj4uQLe4lFm\nVNV3gXebjPttC/POaTL8e+D3HYyvXZZvz+dQRa2VdozpQSIjIwGn6jB37lxeeeWVVudvXKJpuOe+\nJy6//HJuuummVudpfO/+oKCgw8NBQUGH79d/zTXXsGTJEiZNmsTzzz9/1I3fNm7cSFxcHPv27Wsz\nnqalpobhps8P8PZzAvyqR+7S9fuIjQhl9qgEX4dijGmnGTNmsGLFCjIzMwGnBr9t27Zj5ktKSmLL\nli24XC7efPPNVte5ffv2w+/feecdRo4c2ek4S0tLGTBgALW1tbz00kuHx69evZr33nuPb775hgcf\nfJBdu3a1up5XX30VgOXLlxMbG0tsbNc85MlvaiCVNfV8uPkAF0waSK8QvzqWGRMQEhISeP7557ni\niiuorq4G4P7772fUqFFHzffAAw9w/vnnk5CQQFpaWrMXexs8+uijfPzxx4SGhtK3b18WLlzY6Tjv\nu+8+pk+fTkJCAtOnT6e0tJTq6mquv/56nnvuOQYOHMhDDz3EddddxyeffNLixePw8HCmTJlCbW0t\nzz77bKfj8pSodqqFpNelpaVpR9qv5pZUcd87W7hq+hCmD4s7DpEZ41+2bNnC2LFjfR2G6YDmPjsR\nWauqaS0scpjfnOknxoTzf1dM8XUYxhjTrflN0jfGBK7f//73LF68+Khxl156KXfdddcx837wwQf8\n+te/Pmpcampqm9cHOuKWW25hxYoVR4277bbbuPbaa72+LU/5TXnHGNM+W7ZsYcyYMa12WDLdj6qy\ndevWDpd37IqnMQEqPDycgoICutuJn2mZqlJQUEB4eHiH12HlHWMC1KBBg8jOzsYbNzk0XSc8PJxB\ngwZ1eHlL+sYEqNDQUFJTU30dhuliVt4xxpgAYknfGGMCiCV9Y4wJIN2uyaaI5AGdubVePJDvpXC6\nA3/bH/C/ffK3/QH/2yd/2x84dp+GqmqbNx7rdkm/s0Qk3ZO2qj2Fv+0P+N8++dv+gP/tk7/tD3R8\nn6y8Y4wxAcSSvjHGBBB/TPpP+joAL/O3/QH/2yd/2x/wv33yt/2BDu6T39X0jTHGtMwfz/SNMca0\nwJK+McYEEL9J+iIyT0S+FZFMEbnd1/F4g4hkichGEVknIj3uftMi8qyI5IrIpkbj+onIRyKy3f2z\nry9jbK8W9uluEclxf07rRORcX8bYHiIyWEQ+FZEMEdksIre5x/fIz6mV/enJn1G4iKwWkfXufbrH\nPT5VRL5y57xXRaSXR+vzh5q+iAQD24C5QDawBrhCVTN8GlgniUgWkKaqPbJTiYjMBsqAF1R1gnvc\nn4FCVX3AfXDuq6q/bm093UkL+3Q3UKaqD/oyto4QkQHAAFX9WkSigbXARcA19MDPqZX9uYye+xkJ\nEKmqZSISCiwHbgN+BryhqotE5Algvao+3tb6/OVMfxqQqao7VbUGWATM93FMAU9VPwcKm4yeDzQ8\nnXohzj9kj9HCPvVYqrpfVb92vy8FtgDJ9NDPqZX96bHU0fD091D3S4EzgH+7x3v8GflL0k8G9jYa\nzqaHf9BuCnwoImtF5AZfB+MlSaq63/3+AJDky2C86FYR2eAu//SIUkhTIpICTAG+wg8+pyb7Az34\nMxKRYBFZB+QCHwE7gCJVrXPP4nHO85ek769mqeqJwDnALe7Sgt9Qp7bY8+uL8DgwHJgM7Ace8m04\n7SciUcDrwE9VtaTxtJ74OTWzPz36M1LVelWdDAzCqWyM6ei6/CXp5wCDGw0Pco/r0VQ1x/0zF3gT\n58Pu6Q66664N9ddcH8fTaap60P1P6QKeood9Tu468evAS6r6hnt0j/2cmtufnv4ZNVDVIuBTYCbQ\nR0QaHoTlcc7zl6S/BhjpvprdC7gcWOrjmDpFRCLdF6IQkUjgLGBT60v1CEuBBe73C4C3fBiLVzQk\nR7fv0oM+J/dFwmeALar6cKNJPfJzaml/evhnlCAifdzvI3AarGzBSf6XuGfz+DPyi9Y7AO4mWI8A\nwcCzqvp7H4fUKSIyDOfsHpzHWr7c0/ZJRF4B5uDcAvYg8DtgCfAaMATnFtqXqWqPuTDawj7NwSkb\nKJAF/KRRPbxbE5FZwBfARsDlHn0nTh28x31OrezPFfTcz+gEnAu1wTgn6q+p6r3uHLEI6Ad8A1yl\nqtVtrs9fkr4xxpi2+Ut5xxhjjAcs6RtjTACxpG+MMQHEkr4xxgQQS/rGGBNALOkbY0wAsaRvjDEB\n5P8HWvWyfE2q0ZwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"LOkAV4HLAnFu","colab_type":"code","outputId":"eebd096d-69b1-40d6-e037-bb0c7bddee31","executionInfo":{"status":"ok","timestamp":1570788902747,"user_tz":-180,"elapsed":293260,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["for experiment_id in losses.keys():\n","  plt.plot(losses[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('Val Loss')"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Val Loss')"]},"metadata":{"tags":[]},"execution_count":20},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJMfGxrrg1BULCfBjcKdo7e+ulGq2XJHc04D5xph8Y0wWsBg40wXHrZcR3W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KAXfj6F8n9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\nkCZ3pZTyQprclVLKC/0/j3RhaJoHq4wAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"_PoHHLW0BtVe","colab_type":"code","colab":{}},"source":["class CIFARNet(torch.nn.Module):\n","  def __init__(self):\n","    super().__init__()\n","    self.batch_norm0 = torch.nn.BatchNorm2d(3)\n","    \n","    self.conv1 = torch.nn.Conv2d(3, 16, 3, padding=1)\n","    self.act1 = torch.nn.ReLU()\n","    self.batch_norm1 = torch.nn.BatchNorm2d(16)\n","    self.pool1 = torch.nn.MaxPool2d(2, 2)\n","    \n","    self.conv2 = torch.nn.Conv2d(16, 32, 3, padding=1)\n","    self.act2 = torch.nn.ReLU()\n","    self.batch_norm2 = torch.nn.BatchNorm2d(32)\n","    self.pool2 = torch.nn.MaxPool2d(2, 2)\n","    \n","    self.conv3 = torch.nn.Conv2d(32, 64, 3, padding=1)\n","    self.act3 = torch.nn.ReLU()\n","    self.batch_norm3 = torch.nn.BatchNorm2d(64)\n","    \n","    self.fc1 = torch.nn.Linear(64 * 8 * 8, 256)\n","    self.act4 = torch.nn.Tanh()\n","    self.batch_norm4 = torch.nn.BatchNorm1d(256)\n","    \n","    self.fc2 = torch.nn.Linear(256, 64)\n","    self.act5 = torch.nn.Tanh()\n","    self.batch_norm5 = torch.nn.BatchNorm1d(64)\n","    \n","    self.fc3 = torch.nn.Linear(64, 10)\n","    \n","  def forward(self, x):\n","    x = self.batch_norm0(x)\n","    \n","    x = self.conv1(x)\n","    x = self.act1(x)\n","    x = self.batch_norm1(x)\n","    x = self.pool1(x)\n","    \n","    x = self.conv2(x)\n","    x = self.act2(x)\n","    x = self.batch_norm2(x)\n","    x = self.pool2(x)\n","    \n","    x = self.conv3(x)\n","    x = self.act3(x)\n","    x = self.batch_norm3(x)\n","    \n","    x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))\n","    \n","    x = self.fc1(x)\n","    x = self.act4(x)\n","    x = self.batch_norm4(x)\n","    \n","    x = self.fc2(x)\n","    x = self.act5(x)\n","    x = self.batch_norm5(x)\n","    \n","    x = self.fc3(x)\n","    \n","    return x \n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QxcEbFxyF9Q0","colab_type":"code","outputId":"f80ca941-cb74-43e4-8814-840d2d7cb9a6","executionInfo":{"status":"ok","timestamp":1570789100766,"user_tz":-180,"elapsed":489551,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":544}},"source":["accuracies['cifar_net'], losses['cifar_net'] = train(CIFARNet(), \n","                                                     X_train, y_train,\n","                                                     X_test, y_test)"],"execution_count":22,"outputs":[{"output_type":"stream","text":["tensor(0.6617)\n","tensor(0.7072)\n","tensor(0.7109)\n","tensor(0.7226)\n","tensor(0.7230)\n","tensor(0.7208)\n","tensor(0.7192)\n","tensor(0.7147)\n","tensor(0.7204)\n","tensor(0.7176)\n","tensor(0.7144)\n","tensor(0.7125)\n","tensor(0.7093)\n","tensor(0.7211)\n","tensor(0.7253)\n","tensor(0.7204)\n","tensor(0.7206)\n","tensor(0.7191)\n","tensor(0.7198)\n","tensor(0.7171)\n","tensor(0.7227)\n","tensor(0.7178)\n","tensor(0.7202)\n","tensor(0.7227)\n","tensor(0.7136)\n","tensor(0.7178)\n","tensor(0.7109)\n","tensor(0.7144)\n","tensor(0.7198)\n","tensor(0.7247)\n","-----so... training is over----\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"70oAwJ5DHMeX","colab_type":"code","outputId":"e30f79f8-9149-4862-cadf-c91667d865b9","executionInfo":{"status":"ok","timestamp":1570789101130,"user_tz":-180,"elapsed":489267,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["for experiment_id in accuracies.keys():\n","  plt.plot(accuracies[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('Val Accuracy')"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Val Accuracy')"]},"metadata":{"tags":[]},"execution_count":23},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3sN9K+5jfcl6Zg6dyb3T7iUuJM7dYZ2w6uZqmmxNxAbH9up+W9paOq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91D1NK9U9OJX1j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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"UaXA-Ee-HpGb","colab_type":"code","outputId":"01f0a8d2-b1a0-45b9-e43e-116876dcef72","executionInfo":{"status":"ok","timestamp":1570789101137,"user_tz":-180,"elapsed":488743,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["for experiment_id in losses.keys():\n","  plt.plot(losses[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('Val Loss')"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Val Loss')"]},"metadata":{"tags":[]},"execution_count":24},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncluM0shH+z/izV1v0jG4I4v6LSLIPcjcYdkkdVWjjL+HE+/cE8nj3+zn09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c7gc7eDjsB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l1L7lvHt/fBpG/jjSwgfoF0sHverNs2/vDouiqL8hcUld4DhjYfjaOfIktgl5g6lSnx9/Gv2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\nALLibowxAfQ/MaN8puUTHP8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"U44GyrNwIZjd","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":544},"outputId":"c76074c3-77c8-4568-9185-f515060f7b60","executionInfo":{"status":"ok","timestamp":1570790264488,"user_tz":-180,"elapsed":1651673,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["resnet18 = torchvision.models.resnet18()\n","\n","accuracies['resnet18'], losses['resnet18'] = train(resnet18, \n","                                                   X_train, y_train, \n","                                                   X_test, y_test)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["tensor(0.4519)\n","tensor(0.6189)\n","tensor(0.6122)\n","tensor(0.6995)\n","tensor(0.6961)\n","tensor(0.7286)\n","tensor(0.7402)\n","tensor(0.6635)\n","tensor(0.7400)\n","tensor(0.7230)\n","tensor(0.7471)\n","tensor(0.7393)\n","tensor(0.7483)\n","tensor(0.7506)\n","tensor(0.7210)\n","tensor(0.7342)\n","tensor(0.7552)\n","tensor(0.7358)\n","tensor(0.7468)\n","tensor(0.7496)\n","tensor(0.7402)\n","tensor(0.7534)\n","tensor(0.7491)\n","tensor(0.7461)\n","tensor(0.7442)\n","tensor(0.7580)\n","tensor(0.7413)\n","tensor(0.7478)\n","tensor(0.7448)\n","tensor(0.7520)\n","-----so... training is over----\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cV7wJ14QHBGV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":298},"outputId":"6ea32c3f-79d4-4aec-847c-84aab5550d8a","executionInfo":{"status":"ok","timestamp":1570790264859,"user_tz":-180,"elapsed":1651511,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["for experiment_id in accuracies.keys():\n","  plt.plot(accuracies[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('val accuracy')"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'val accuracy')"]},"metadata":{"tags":[]},"execution_count":26},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb72VgGlTe2yzPTubqv++TN1nn4FOR+hZZxE851Qqnn4G24YNBJ50In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P/TJCp4y3muoLH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\npPJP+mt2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fPPNF6z//PPP6datGxEREfTp04d9+/ZVZKjlypOk3wI4VGg50b2ugIh0A1qq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\nzFmo0fntGQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"UfRCxC6_JZla","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":298},"outputId":"da3a6b08-15d5-4f3c-f790-47e154ebd8c1","executionInfo":{"status":"ok","timestamp":1570790265356,"user_tz":-180,"elapsed":1651466,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["for experiment_id in losses.keys():\n","  plt.plot(losses[experiment_id], label=experiment_id)\n","plt.legend()\n","plt.title('val loss')"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'val loss')"]},"metadata":{"tags":[]},"execution_count":27},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMW3atCI/Py8vD6VSWeo4QkJCOHnyJL169Sr1uWTlL1oTzcIzC9l+fTuO5o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S++hrmzfxw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\nyMjtXSruMgVlImYOHKyW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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"zp8jLz2gDTlf","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}